39 citations · 117 across the 43 of their papers we have counts for
43 papers
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Chuning Zhu, Max Simchowitz, Siri Gadipudi +1
Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them s…
Compositional Foundation Models for Hierarchical Planning
Anurag Ajay, Seungwook Han, Yilun Du +7
To make effective decisions in novel environments with long-horizon goals, it is crucial to engage in hierarchical reasoning across spatial and temporal scales. This entails planni…
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo +3
Dexterous manipulation tasks involving contact-rich interactions pose a significant challenge for both model-based control systems and imitation learning algorithms. The complexity…
Maximizing Success Rate of Payment Routing using Non-stationary Bandits
Aayush Chaudhary, Abhinav Rai, Abhishek Gupta
This paper discusses the system architecture design and deployment of non-stationary multi-armed bandit approaches to determine a near-optimal payment routing policy based on the r…
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective
Max Simchowitz, Abhishek Gupta, Kaiqing Zhang
Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distributio…
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
Marcel Torne, Max Balsells, Zihan Wang +4
Exploration and reward specification are fundamental and intertwined challenges for reinforcement learning. Solving sequential decision-making tasks requiring expansive exploration…
Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games
Arti Vedula, Abhishek Gupta, Shaileshh Bojja Venkatakrishnan
Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Mine…
Empirical Measurement of Aesthetic Experience of Music
Abhishek Gupta, C. M. Markan
Chills or goosebumps, also called frisson, is a phenomenon that is often associated with an aesthetic experience e.g., music or some other ecstatic experience. The temporal and spa…
Navigating Fairness in Radiology AI: Concepts, Consequences,and Crucial Considerations
Vasantha Kumar Venugopal, Abhishek Gupta, Rohit Takhar +3
Artificial Intelligence (AI) has significantly revolutionized radiology, promising improved patient outcomes and streamlined processes. However, it's critical to ensure the fairnes…
Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
H. J. Terry Suh, Glen Chou, Hongkai Dai +3
Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinfo…
Self-Supervised Reinforcement Learning that Transfers using Random Features
Boyuan Chen, Chuning Zhu, Pulkit Agrawal +2
Model-free reinforcement learning algorithms have exhibited great potential in solving single-task sequential decision-making problems with high-dimensional observations and long h…
Learning to Extrapolate: A Transductive Approach
Aviv Netanyahu, Abhishek Gupta, Max Simchowitz +2
Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. Howe…
Augmented Collective Intelligence in Collaborative Ideation: Agenda and Challenges
Emily Dardaman, Abhishek Gupta
AI systems may be better thought of as peers than as tools. This paper explores applications of augmented collective intelligence (ACI) beneficial to collaborative ideation. Design…
Asking Better Questions -- The Art and Science of Forecasting: A mechanism for truer answers to high-stakes questions
Emily Dardaman, Abhishek Gupta
Without the ability to estimate and benchmark AI capability advancements, organizations are left to respond to each change reactively, impeding their ability to build viable mid an…
Cherry-Picking with Reinforcement Learning : Robust Dynamic Grasping in Unstable Conditions
Yunchu Zhang, Liyiming Ke, Abhay Deshpande +2
Grasping small objects surrounded by unstable or non-rigid material plays a crucial role in applications such as surgery, harvesting, construction, disaster recovery, and assisted…
GenAug: Retargeting behaviors to unseen situations via Generative Augmentation
Zoey Chen, Sho Kiami, Abhishek Gupta +1
Robot learning methods have the potential for widespread generalization across tasks, environments, and objects. However, these methods require large diverse datasets that are expe…
Guiding Pretraining in Reinforcement Learning with Large Language Models
Yuqing Du, Olivia Watkins, Zihan Wang +5
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by…
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
Kelvin Xu, Zheyuan Hu, Ria Doshi +4
Complex and contact-rich robotic manipulation tasks, particularly those that involve multi-fingered hands and underactuated object manipulation, present a significant challenge to…
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping polici…
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Abhishek Gupta, Dibya Ghosh +2
Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that quickly adapt to many tasks with varying rewards or dynamics functions. However, learned m…
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-l…
Superior mechanical properties by exploiting size-effects and multiscale interactions in hierarchically architected foams
Komal Chawla, Abhishek Gupta, Abhijeet S. Bhardwaj +1
Protective applications in extreme environments demand thermally stable materials with superior modulus, strength, and specific energy absorption (SEA) at lightweight. However, the…
Fundamentals of Vehicular Communication Networks with Vehicle Platoons
Kaushlendra Pandey, Kanaka Raju Perumalla, Abhishek K. Gupta +1
Vehicular platooning is a promising way to facilitate efficient movement of vehicles with a shared route. Despite its relevance, the interplay of platooning and the communication p…